Perception of difficulty with Random Curve

## Ignorer les outliers relatifs à l'utilisation de l'échelle de confiance :  TRUE

## Résultats basés sur la l'échelle de confiance :  TRUE

## Nombre de participants à l'expérimentation :  58

## Nombre de participants se déclarant comme joueurs :  29

## Nombre de femmes se déclarant comme joueuses :  3

## Age médian des joueurs :  15

Removing Outliers

## [1] "Outliers BET STANDARD DEVIATION: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers BET SAVED SHEEPS: "

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers CS STANDARD DEVIATION: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806, 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, dyg7cga2o, dyg7cga2o, ejodnl05c, kctu3te1y, tmxmxmwhi, zp9bc59o5, zv35u39vc"

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers CS NULL: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806, 9b3ph38yc, 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, bzrji9dqz, dyg7cga2o, dyg7cga2o, dyg7cga2o, ejodnl05c, kctu3te1y, kctu3te1y, m4ye7uz5h, qzh5zi9e8, tmxmxmwhi, tmxmxmwhi, zp9bc59o5, zp9bc59o5, zv35u39vc"

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Total number of participants :  58"

## [1] "Total number of outliers:  15"

## [1] "- total number of outliers motor task:  11"

## [1] "- total number of outliers perceptive task:  6"

## [1] "- total number of outliers logical task:  8"

## [1] "Total number of participants after removing outliers:  55"

## [1] "- motor:  47"

## [1] "- perceptive:  50"

## [1] "- logical:  52"

Modeling difficulty

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1669.2   1690.0   -830.6   1661.2     1359 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8343 -0.7720  0.3062  0.7571  2.7501 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.4686   0.6846  
## Number of obs: 1363, groups:  IDjoueur, 47
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -0.9982     0.1974  -5.057 4.27e-07 ***
## difficulty    2.8413     0.2301  12.346  < 2e-16 ***
## timeNorm     -0.5530     0.2179  -2.538   0.0112 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.549       
## timeNorm   -0.577 -0.022

## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.

## 
##  Logique2   Motrice Sensoriel 
##         0      1363         0 
## [1] "Player levels from ranef:"
##   (Intercept)      
##  Min.   :-0.96344  
##  1st Qu.:-0.37670  
##  Median :-0.08364  
##  Mean   :-0.00173  
##  3rd Qu.: 0.21652  
##  Max.   : 1.57591  
## [1] "Intercept: -0.998 4.3e-07 ***"
## [1] "Difficulty: 2.84 5.1e-35 ***"
## [1] "Time: -0.553 0.011 *"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.26"
## [1] "Cross Val: 0.67"
## [1] "AIC: 1700"

##          0%         25%         50%         75%        100% 
## -1.57590869 -0.21652213  0.08364306  0.37669604  0.96343671

##          0%         25%         50%         75%        100% 
## -1.57590869 -0.21652213  0.08364306  0.37669604  0.96343671

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1123.8   1144.9   -557.9   1115.8     1446 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3468 -0.3664  0.1130  0.3424  6.3198 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.788    0.8877  
## Number of obs: 1450, groups:  IDjoueur, 50
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.1933     0.2727 -11.710   <2e-16 ***
## difficulty    8.1870     0.4266  19.192   <2e-16 ***
## timeNorm     -0.4773     0.2844  -1.679   0.0932 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.633       
## timeNorm   -0.506 -0.072

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.0241954 (tol =
## 0.001, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

## The result is correct only if all data used by the model has not changed since model was fitted.

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0241954 (tol = 0.001, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

## The result is correct only if all data used by the model has not changed since model was fitted.

## 
##  Logique2   Motrice Sensoriel 
##         0         0      1450 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.7107216  
##  1st Qu.:-0.4707794  
##  Median : 0.0814227  
##  Mean   :-0.0009546  
##  3rd Qu.: 0.4563319  
##  Max.   : 1.5481412  
## [1] "Intercept: -3.19 1.1e-31 ***"
## [1] "Difficulty: 8.19 4.3e-82 ***"
## [1] "Time: -0.477 0.093 ."
## [1] "R2 fixed: 0.32"
## [1] "R2 mixed: 0.46"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1100"

##          0%         25%         50%         75%        100% 
## -1.54814123 -0.45633191 -0.08142269  0.47077942  1.71072162

##          0%         25%         50%         75%        100% 
## -1.54814123 -0.45633191 -0.08142269  0.47077942  1.71072162

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1426.5   1447.8   -709.2   1418.5     1504 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.9435 -0.5021 -0.1156  0.5089  4.9862 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.577    1.256   
## Number of obs: 1508, groups:  IDjoueur, 52
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.8650     0.2652  -7.033 2.01e-12 ***
## difficulty    5.6686     0.3206  17.680  < 2e-16 ***
## timeNorm     -1.9313     0.2573  -7.507 6.04e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.496       
## timeNorm   -0.378 -0.227

## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.

## 
##  Logique2   Motrice Sensoriel 
##      1508         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.7902825  
##  1st Qu.:-0.7784485  
##  Median :-0.3355504  
##  Mean   :-0.0003123  
##  3rd Qu.: 0.7369882  
##  Max.   : 3.1275699  
## [1] "Intercept: -1.86 2e-12 ***"
## [1] "Difficulty: 5.67 6e-70 ***"
## [1] "Time: -1.93 6e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1400"

##         0%        25%        50%        75%       100% 
## -3.1275699 -0.7369882  0.3355504  0.7784485  1.7902825

##         0%        25%        50%        75%       100% 
## -3.1275699 -0.7369882  0.3355504  0.7784485  1.7902825

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.37495, p-value = 0.7077
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04294701

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.91836, p-value = 0.3584
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1023712

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.30458, p-value = 0.7607
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03301126

Playing board games in general and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.99227, p-value = 0.3211
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##    tau 
## 0.1118

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.31221, p-value = 0.7549
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03415935

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.79975, p-value = 0.4239
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.08596507

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 23 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.24953, p-value = 0.8029
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03718731

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 23 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.4333, p-value = 0.01496
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3398094 
## 
## [1] "self.eff.on.level.s 0.34 0.015 *"

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 26 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.51036, p-value = 0.6098
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07281435

Risk aversion and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3418, p-value = 0.1797
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1465938

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.0586, p-value = 0.03953
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2157658 
## 
## [1] "risk.av.on.level.s 0.22 0.04 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3062, p-value = 0.1915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1347244

Age and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.3062, p-value = 0.1915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1372263

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 1 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.8963, p-value = 0.05791
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1937968 
## 
## [1] "age.on.level.s 0.19 0.058 ."

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2774, p-value = 0.2015
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1275074

Sex and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.0369, p-value = 0.04166
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2478106 
## 
## [1] "sexe.on.level.m -0.25 0.042 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.083189, p-value = 0.9337
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## 0.009799919

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.26928, p-value = 0.7877
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03108211

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 163, p-value = 0.04192
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.73654416 -0.04033621
## sample estimates:
## difference in location 
##             -0.3800085 
## 
## [1] "sexe.on.level.m.2 -0.38 0.042 * mean(A): 0.15 mean(B): -0.27"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 276, p-value = 0.9426
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.4761356  0.5715623
## sample estimates:
## difference in location 
##             0.01423148

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 292, p-value = 0.7971
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.8271571  0.5994594
## sample estimates:
## difference in location 
##            -0.04046848

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.069 44   0.0032 **
##  2:      0.09375          0.110 53 0.00022 ***
##  3:      0.15625          0.094 54   0.0016 **
##  4:      0.21875          0.110 52 0.00013 ***
##  5:      0.28125          0.097 54   0.0015 **
##  6:      0.34375          0.110 52 3.2e-05 ***
##  7:      0.40625          0.074 53     0.044 *
##  8:      0.46875          0.019 52     0.46 :(
##  9:      0.53125         -0.024 51     0.41 :(
## 10:      0.59375         -0.047 55     0.024 *
## 11:      0.65625         -0.073 52   0.0013 **
## 12:      0.71875         -0.130 54 8.7e-06 ***
## 13:      0.78125         -0.160 53 2.1e-07 ***
## 14:      0.84375         -0.210 52   3e-08 ***
## 15:      0.90625         -0.230 54 7.8e-10 ***
## 16:      0.96875         -0.170 54 3.7e-09 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 44   0.0032 **
##  2: 53 0.00022 ***
##  3: 54   0.0016 **
##  4: 52 0.00013 ***
##  5: 54   0.0015 **
##  6: 52 3.2e-05 ***
##  7: 53     0.044 *
##  8: 52     0.46 :(
##  9: 51     0.41 :(
## 10: 55     0.024 *
## 11: 52   0.0013 **
## 12: 54 8.7e-06 ***
## 13: 53 2.1e-07 ***
## 14: 52   3e-08 ***
## 15: 54 7.8e-10 ***
## 16: 54 3.7e-09 ***
## [1] 52.4
## [1] 2.5

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.069 30      0.04 *
##  2:      0.09375          0.081 31     0.034 *
##  3:      0.15625          0.094 36     0.088 .
##  4:      0.21875          0.077 35      0.04 *
##  5:      0.28125          0.079 32     0.031 *
##  6:      0.34375          0.110 32   0.0014 **
##  7:      0.40625          0.069 35     0.034 *
##  8:      0.46875          0.040 33     0.11 :(
##  9:      0.53125          0.019 31     0.73 :(
## 10:      0.59375         -0.044 36     0.23 :(
## 11:      0.65625         -0.073 31     0.053 .
## 12:      0.71875         -0.180 34 8.6e-05 ***
## 13:      0.78125         -0.170 32   0.0018 **
## 14:      0.84375         -0.220 23 0.00084 ***
## 15:      0.90625         -0.250 22 0.00019 ***
## 16:      0.96875         -0.120 13     0.068 .
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 30      0.04 *
##  2: 31     0.034 *
##  3: 36     0.088 .
##  4: 35      0.04 *
##  5: 32     0.031 *
##  6: 32   0.0014 **
##  7: 35     0.034 *
##  8: 33     0.11 :(
##  9: 31     0.73 :(
## 10: 36     0.23 :(
## 11: 31     0.053 .
## 12: 34 8.6e-05 ***
## 13: 32   0.0018 **
## 14: 23 0.00084 ***
## 15: 22 0.00019 ***
## 16: 13     0.068 .
## [1] 30.4
## [1] 6.12

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.052 27     0.041 *
##  2:      0.09375          0.140 30   0.0016 **
##  3:      0.15625          0.094 30     0.066 .
##  4:      0.21875          0.110 34      0.01 *
##  5:      0.28125          0.094 32     0.083 .
##  6:      0.34375          0.073 31     0.15 :(
##  7:      0.40625          0.034 34     0.66 :(
##  8:      0.46875         -0.019 33     0.77 :(
##  9:      0.53125         -0.015 33     0.68 :(
## 10:      0.59375         -0.094 32     0.046 *
## 11:      0.65625         -0.160 35   2e-04 ***
## 12:      0.71875         -0.100 34   0.0017 **
## 13:      0.78125         -0.140 35 0.00016 ***
## 14:      0.84375         -0.220 33 2.9e-05 ***
## 15:      0.90625         -0.230 30 4.4e-06 ***
## 16:      0.96875         -0.140 30 4.6e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 27     0.041 *
##  2: 30   0.0016 **
##  3: 30     0.066 .
##  4: 34      0.01 *
##  5: 32     0.083 .
##  6: 31     0.15 :(
##  7: 34     0.66 :(
##  8: 33     0.77 :(
##  9: 33     0.68 :(
## 10: 32     0.046 *
## 11: 35   2e-04 ***
## 12: 34   0.0017 **
## 13: 35 0.00016 ***
## 14: 33 2.9e-05 ***
## 15: 30 4.4e-06 ***
## 16: 30 4.6e-05 ***
## [1] 32.1
## [1] 2.24

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.019  2        1 :(
##  2:      0.09375          0.081 13     0.23 :(
##  3:      0.15625          0.120 15     0.024 *
##  4:      0.21875          0.031 12     0.22 :(
##  5:      0.28125          0.220 15     0.056 .
##  6:      0.34375          0.160 13   0.0026 **
##  7:      0.40625          0.094 15     0.15 :(
##  8:      0.46875          0.044 15     0.35 :(
##  9:      0.53125         -0.091 17     0.045 *
## 10:      0.59375         -0.057 19     0.89 :(
## 11:      0.65625          0.010 17      0.6 :(
## 12:      0.71875         -0.094 20     0.088 .
## 13:      0.78125         -0.110 19     0.055 .
## 14:      0.84375         -0.210 21 0.00055 ***
## 15:      0.90625         -0.210 23 0.00021 ***
## 16:      0.96875         -0.270 22 4.8e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  2        1 :(
##  2: 13     0.23 :(
##  3: 15     0.024 *
##  4: 12     0.22 :(
##  5: 15     0.056 .
##  6: 13   0.0026 **
##  7: 15     0.15 :(
##  8: 15     0.35 :(
##  9: 17     0.045 *
## 10: 19     0.89 :(
## 11: 17      0.6 :(
## 12: 20     0.088 .
## 13: 19     0.055 .
## 14: 21 0.00055 ***
## 15: 23 0.00021 ***
## 16: 22 4.8e-05 ***
## [1] 16.1
## [1] 5.03

Motor task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375        -0.0440  5 0.78 :(
##  3:      0.15625        -0.0730 19 0.13 :(
##  4:      0.21875         0.0190 35 0.65 :(
##  5:      0.28125         0.0350 40 0.36 :(
##  6:      0.34375         0.0900 40 0.018 *
##  7:      0.40625         0.0540 42 0.19 :(
##  8:      0.46875         0.0560 42 0.098 .
##  9:      0.53125         0.0440 43 0.15 :(
## 10:      0.59375        -0.0100 45 0.91 :(
## 11:      0.65625        -0.0560 44 0.041 *
## 12:      0.71875        -0.0440 43 0.076 .
## 13:      0.78125        -0.0810 38 0.032 *
## 14:      0.84375        -0.1400 23 0.023 *
## 15:      0.90625        -0.0063  7 0.44 :(
## 16:      0.96875        -0.2400  4  0.2 :(
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  5 0.78 :(
##  2: 19 0.13 :(
##  3: 35 0.65 :(
##  4: 40 0.36 :(
##  5: 40 0.018 *
##  6: 42 0.19 :(
##  7: 42 0.098 .
##  8: 43 0.15 :(
##  9: 45 0.91 :(
## 10: 44 0.041 *
## 11: 43 0.076 .
## 12: 38 0.032 *
## 13: 23 0.023 *
## 14:  7 0.44 :(
## 15:  4  0.2 :(
## [1] 31.3
## [1] 15.4

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375        -0.0440  5 0.78 :(
##  3:      0.15625        -0.0730 17 0.057 .
##  4:      0.21875        -0.0190 21 0.61 :(
##  5:      0.28125         0.0190 21 0.42 :(
##  6:      0.34375         0.1100 21 0.023 *
##  7:      0.40625         0.0600 20 0.16 :(
##  8:      0.46875         0.1100 20 0.024 *
##  9:      0.53125         0.1000 19 0.067 .
## 10:      0.59375         0.0880 20 0.18 :(
## 11:      0.65625         0.0051 20    1 :(
## 12:      0.71875        -0.0190 17  0.6 :(
## 13:      0.78125        -0.0560 12 0.25 :(
## 14:      0.84375             NA  0      NA
## 15:      0.90625             NA  0      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  5 0.78 :(
##  2: 17 0.057 .
##  3: 21 0.61 :(
##  4: 21 0.42 :(
##  5: 21 0.023 *
##  6: 20 0.16 :(
##  7: 20 0.024 *
##  8: 19 0.067 .
##  9: 20 0.18 :(
## 10: 20    1 :(
## 11: 17  0.6 :(
## 12: 12 0.25 :(
## [1] 17.8
## [1] 4.77

## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  0        NA
##  3:      0.15625          0.290  2      1 :(
##  4:      0.21875          0.069 14   0.29 :(
##  5:      0.28125          0.069 19   0.46 :(
##  6:      0.34375          0.076 19   0.32 :(
##  7:      0.40625          0.020 21   0.83 :(
##  8:      0.46875         -0.019 20   0.93 :(
##  9:      0.53125          0.019 20   0.69 :(
## 10:      0.59375         -0.077 20   0.076 .
## 11:      0.65625         -0.160 20 0.0074 **
## 12:      0.71875         -0.056 21   0.088 .
## 13:      0.78125         -0.081 21   0.21 :(
## 14:      0.84375         -0.160 18   0.029 *
## 15:      0.90625         -0.210  2    0.5 :(
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  2      1 :(
##  2: 14   0.29 :(
##  3: 19   0.46 :(
##  4: 19   0.32 :(
##  5: 21   0.83 :(
##  6: 20   0.93 :(
##  7: 20   0.69 :(
##  8: 20   0.076 .
##  9: 20 0.0074 **
## 10: 21   0.088 .
## 11: 21   0.21 :(
## 12: 18   0.029 *
## 13:  2    0.5 :(
## [1] 16.7
## [1] 6.77

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_errorbar).

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 0      NA
##  7:      0.40625             NA 1      NA
##  8:      0.46875         0.1800 2  0.5 :(
##  9:      0.53125        -0.0310 4 0.58 :(
## 10:      0.59375        -0.0270 5 0.78 :(
## 11:      0.65625        -0.0059 4    1 :(
## 12:      0.71875        -0.0520 5 0.62 :(
## 13:      0.78125        -0.0940 5 0.31 :(
## 14:      0.84375        -0.0440 5 0.59 :(
## 15:      0.90625        -0.0062 5    1 :(
## 16:      0.96875        -0.2400 4  0.2 :(
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  2  0.5 :(
## 2:  4 0.58 :(
## 3:  5 0.78 :(
## 4:  4    1 :(
## 5:  5 0.62 :(
## 6:  5 0.31 :(
## 7:  5 0.59 :(
## 8:  5    1 :(
## 9:  4  0.2 :(
## [1] 4.33
## [1] 1

## Warning: Removed 7 rows containing missing values (geom_point).

## Warning: Removed 7 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.024 38     0.29 :(
##  2:      0.09375          0.031 47      0.2 :(
##  3:      0.15625          0.044 45     0.47 :(
##  4:      0.21875          0.031 34     0.63 :(
##  5:      0.28125          0.019 32     0.99 :(
##  6:      0.34375         -0.019 28     0.77 :(
##  7:      0.40625         -0.031 32     0.61 :(
##  8:      0.46875         -0.120 31     0.044 *
##  9:      0.53125         -0.180 27   0.0049 **
## 10:      0.59375         -0.190 34 0.00092 ***
## 11:      0.65625         -0.180 33 0.00027 ***
## 12:      0.71875         -0.220 34 5.2e-05 ***
## 13:      0.78125         -0.260 32   9e-06 ***
## 14:      0.84375         -0.270 39 1.6e-05 ***
## 15:      0.90625         -0.210 47 4.8e-08 ***
## 16:      0.96875         -0.094 50 1.2e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 38     0.29 :(
##  2: 47      0.2 :(
##  3: 45     0.47 :(
##  4: 34     0.63 :(
##  5: 32     0.99 :(
##  6: 28     0.77 :(
##  7: 32     0.61 :(
##  8: 31     0.044 *
##  9: 27   0.0049 **
## 10: 34 0.00092 ***
## 11: 33 0.00027 ***
## 12: 34 5.2e-05 ***
## 13: 32   9e-06 ***
## 14: 39 1.6e-05 ***
## 15: 47 4.8e-08 ***
## 16: 50 1.2e-06 ***
## [1] 36.4
## [1] 7.16

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.060 13   0.48 :(
##  2:      0.09375         -0.044 12   0.72 :(
##  3:      0.15625          0.094 12    0.4 :(
##  4:      0.21875         -0.085  6    0.4 :(
##  5:      0.28125          0.140  9   0.28 :(
##  6:      0.34375         -0.094  7   0.27 :(
##  7:      0.40625         -0.110  9   0.12 :(
##  8:      0.46875         -0.220  9   0.096 .
##  9:      0.53125         -0.200  6   0.31 :(
## 10:      0.59375         -0.240  9   0.043 *
## 11:      0.65625         -0.360  8   0.014 *
## 12:      0.71875         -0.470 10 0.0055 **
## 13:      0.78125         -0.310  7   0.051 .
## 14:      0.84375         -0.220 10   0.052 .
## 15:      0.90625         -0.160 11   0.014 *
## 16:      0.96875         -0.120 13   0.12 :(
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 13   0.48 :(
##  2: 12   0.72 :(
##  3: 12    0.4 :(
##  4:  6    0.4 :(
##  5:  9   0.28 :(
##  6:  7   0.27 :(
##  7:  9   0.12 :(
##  8:  9   0.096 .
##  9:  6   0.31 :(
## 10:  9   0.043 *
## 11:  8   0.014 *
## 12: 10 0.0055 **
## 13:  7   0.051 .
## 14: 10   0.052 .
## 15: 11   0.014 *
## 16: 13   0.12 :(
## [1] 9.44
## [1] 2.31

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.019 23     0.58 :(
##  2:      0.09375          0.031 24     0.46 :(
##  3:      0.15625         -0.056 20     0.048 *
##  4:      0.21875          0.031 20     0.84 :(
##  5:      0.28125         -0.031 14     0.66 :(
##  6:      0.34375         -0.023 15     0.71 :(
##  7:      0.40625          0.019 16     0.74 :(
##  8:      0.46875         -0.094 17      0.2 :(
##  9:      0.53125         -0.130 14      0.1 :(
## 10:      0.59375         -0.240 16     0.032 *
## 11:      0.65625         -0.180 18     0.012 *
## 12:      0.71875         -0.190 13   0.0077 **
## 13:      0.78125         -0.260 18 0.00043 ***
## 14:      0.84375         -0.340 19   0.0027 **
## 15:      0.90625         -0.210 24 9.5e-05 ***
## 16:      0.96875         -0.063 24 0.00095 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 23     0.58 :(
##  2: 24     0.46 :(
##  3: 20     0.048 *
##  4: 20     0.84 :(
##  5: 14     0.66 :(
##  6: 15     0.71 :(
##  7: 16     0.74 :(
##  8: 17      0.2 :(
##  9: 14      0.1 :(
## 10: 16     0.032 *
## 11: 18     0.012 *
## 12: 13   0.0077 **
## 13: 18 0.00043 ***
## 14: 19   0.0027 **
## 15: 24 9.5e-05 ***
## 16: 24 0.00095 ***
## [1] 18.4
## [1] 3.78

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.019  2      1 :(
##  2:      0.09375          0.081 11   0.12 :(
##  3:      0.15625          0.120 13    0.03 *
##  4:      0.21875          0.089  8   0.29 :(
##  5:      0.28125         -0.031  9      1 :(
##  6:      0.34375          0.090  6    0.09 .
##  7:      0.40625          0.094  7   0.67 :(
##  8:      0.46875         -0.044  5      1 :(
##  9:      0.53125         -0.280  7   0.021 *
## 10:      0.59375         -0.094  9   0.087 .
## 11:      0.65625         -0.160  7   0.34 :(
## 12:      0.71875         -0.130 11   0.26 :(
## 13:      0.78125         -0.180  7   0.11 :(
## 14:      0.84375         -0.190 10   0.031 *
## 15:      0.90625         -0.290 12 0.0041 **
## 16:      0.96875         -0.200 13 0.0021 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  2      1 :(
##  2: 11   0.12 :(
##  3: 13    0.03 *
##  4:  8   0.29 :(
##  5:  9      1 :(
##  6:  6    0.09 .
##  7:  7   0.67 :(
##  8:  5      1 :(
##  9:  7   0.021 *
## 10:  9   0.087 .
## 11:  7   0.34 :(
## 12: 11   0.26 :(
## 13:  7   0.11 :(
## 14: 10   0.031 *
## 15: 12 0.0041 **
## 16: 13 0.0021 **
## [1] 8.56
## [1] 3.03

Logical task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.089 35     0.017 *
##  2:      0.09375          0.160 40 5.2e-05 ***
##  3:      0.15625          0.150 40 0.00025 ***
##  4:      0.21875          0.230 42 9.5e-06 ***
##  5:      0.28125          0.220 34 0.00028 ***
##  6:      0.34375          0.160 39 5.5e-05 ***
##  7:      0.40625          0.094 44     0.011 *
##  8:      0.46875          0.031 39     0.024 *
##  9:      0.53125         -0.031 37     0.21 :(
## 10:      0.59375         -0.019 41     0.77 :(
## 11:      0.65625         -0.018 39     0.68 :(
## 12:      0.71875         -0.100 38    0.002 **
## 13:      0.78125         -0.160 43 9.5e-05 ***
## 14:      0.84375         -0.220 41 6.5e-07 ***
## 15:      0.90625         -0.260 40 3.4e-07 ***
## 16:      0.96875         -0.340 25 1.4e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 35     0.017 *
##  2: 40 5.2e-05 ***
##  3: 40 0.00025 ***
##  4: 42 9.5e-06 ***
##  5: 34 0.00028 ***
##  6: 39 5.5e-05 ***
##  7: 44     0.011 *
##  8: 39     0.024 *
##  9: 37     0.21 :(
## 10: 41     0.77 :(
## 11: 39     0.68 :(
## 12: 38    0.002 **
## 13: 43 9.5e-05 ***
## 14: 41 6.5e-07 ***
## 15: 40 3.4e-07 ***
## 16: 25 1.4e-05 ***
## [1] 38.6
## [1] 4.47

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.050 26   0.071 .
##  2:      0.09375          0.110 26  0.007 **
##  3:      0.15625          0.110 24   0.027 *
##  4:      0.21875          0.200 24 0.0014 **
##  5:      0.28125          0.140 17   0.13 :(
##  6:      0.34375          0.160 21   0.036 *
##  7:      0.40625          0.120 22   0.085 .
##  8:      0.46875          0.031 20   0.15 :(
##  9:      0.53125         -0.031 18   0.27 :(
## 10:      0.59375         -0.094 21   0.19 :(
## 11:      0.65625         -0.056 17   0.57 :(
## 12:      0.71875         -0.120 18   0.026 *
## 13:      0.78125         -0.160 21 0.0081 **
## 14:      0.84375         -0.220 18 0.0018 **
## 15:      0.90625         -0.310 15 0.0024 **
## 16:      0.96875             NA  1        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 26   0.071 .
##  2: 26  0.007 **
##  3: 24   0.027 *
##  4: 24 0.0014 **
##  5: 17   0.13 :(
##  6: 21   0.036 *
##  7: 22   0.085 .
##  8: 20   0.15 :(
##  9: 18   0.27 :(
## 10: 21   0.19 :(
## 11: 17   0.57 :(
## 12: 18   0.026 *
## 13: 21 0.0081 **
## 14: 18 0.0018 **
## 15: 15 0.0024 **
## [1] 20.5
## [1] 3.4

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.140  9   0.15 :(
##  2:      0.09375          0.360 12 0.0031 **
##  3:      0.15625          0.340 13 0.0026 **
##  4:      0.21875          0.330 13 0.0031 **
##  5:      0.28125          0.220 10 0.0056 **
##  6:      0.34375          0.160 10 0.0067 **
##  7:      0.40625          0.094 13   0.35 :(
##  8:      0.46875          0.031 11   0.049 *
##  9:      0.53125         -0.031 10   0.75 :(
## 10:      0.59375          0.031 10   0.36 :(
## 11:      0.65625         -0.110 12   0.12 :(
## 12:      0.71875         -0.220 12   0.053 .
## 13:      0.78125         -0.240 13 0.0039 **
## 14:      0.84375         -0.220 13 0.0026 **
## 15:      0.90625         -0.250 13 0.0032 **
## 16:      0.96875         -0.340 12 0.0031 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  9   0.15 :(
##  2: 12 0.0031 **
##  3: 13 0.0026 **
##  4: 13 0.0031 **
##  5: 10 0.0056 **
##  6: 10 0.0067 **
##  7: 13   0.35 :(
##  8: 11   0.049 *
##  9: 10   0.75 :(
## 10: 10   0.36 :(
## 11: 12   0.12 :(
## 12: 12   0.053 .
## 13: 13 0.0039 **
## 14: 13 0.0026 **
## 15: 13 0.0032 **
## 16: 12 0.0031 **
## [1] 11.6
## [1] 1.41

## [1] "bad"

## Warning: cannot compute confidence interval when all observations are zero
## or tied

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375         -0.094  2   0.35 :(
##  3:      0.15625          0.051  3      1 :(
##  4:      0.21875          0.031  5   0.59 :(
##  5:      0.28125          0.450  7   0.034 *
##  6:      0.34375          0.270  8   0.019 *
##  7:      0.40625          0.240  9   0.096 .
##  8:      0.46875          0.120  8   0.53 :(
##  9:      0.53125         -0.031  9   0.55 :(
## 10:      0.59375          0.031 10   0.68 :(
## 11:      0.65625          0.069 10   0.22 :(
## 12:      0.71875         -0.056  8   0.44 :(
## 13:      0.78125         -0.031  9   0.55 :(
## 14:      0.84375         -0.220 10   0.014 *
## 15:      0.90625         -0.240 12 0.0052 **
## 16:      0.96875         -0.350 12 0.0025 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  2   0.35 :(
##  2:  3      1 :(
##  3:  5   0.59 :(
##  4:  7   0.034 *
##  5:  8   0.019 *
##  6:  9   0.096 .
##  7:  8   0.53 :(
##  8:  9   0.55 :(
##  9: 10   0.68 :(
## 10: 10   0.22 :(
## 11:  8   0.44 :(
## 12:  9   0.55 :(
## 13: 10   0.014 *
## 14: 12 0.0052 **
## 15: 12 0.0025 **
## [1] 8.13
## [1] 2.9

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.75587  -0.18208   0.01722   0.17996   0.67980  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.07834    0.02339   3.350  0.00083 ***
## timeNorm     0.01356    0.02393   0.567  0.57104    
## obj.diff    -0.19206    0.03147  -6.103 1.35e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05882497)
## 
##     Null deviance: 82.357  on 1362  degrees of freedom
## Residual deviance: 80.002  on 1360  degrees of freedom
## AIC: 11.387
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.81669  -0.17979  -0.04371   0.21432   0.82084  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.04155    0.01869   2.223   0.0264 *  
## timeNorm     0.05613    0.02483   2.261   0.0239 *  
## obj.diff    -0.27648    0.01919 -14.407   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06951042)
## 
##     Null deviance: 115.46  on 1449  degrees of freedom
## Residual deviance: 100.58  on 1447  degrees of freedom
## AIC: 253.81
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74305  -0.21400  -0.02148   0.20096   0.71922  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20615    0.02036  10.127  < 2e-16 ***
## timeNorm     0.06739    0.02531   2.662  0.00785 ** 
## obj.diff    -0.51720    0.02162 -23.927  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07044787)
## 
##     Null deviance: 151.98  on 1507  degrees of freedom
## Residual deviance: 106.02  on 1505  degrees of freedom
## AIC: 283.97
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n    pval
##  1:      1.5      0.5414894     0.5916709 -0.041797918  94 0.16 :(
##  2:      4.5      0.5347518     0.5750233 -0.031870515 141 0.16 :(
##  3:      7.5      0.5085106     0.5313589 -0.018540309 141 0.41 :(
##  4:     10.5      0.5404255     0.5341000  0.017660547 141 0.43 :(
##  5:     13.5      0.5085106     0.5167958 -0.006657395 141 0.77 :(
##  6:     16.5      0.5276596     0.5259445  0.002730878 141  0.9 :(
##  7:     19.5      0.4971631     0.5307814 -0.035624644 141 0.081 .
##  8:     22.5      0.4737589     0.4890926 -0.014471503 141  0.5 :(
##  9:     25.5      0.4758865     0.4723221  0.005341319 141 0.81 :(
## 10:     28.5      0.4574468     0.4526413  0.002547420 141 0.88 :(
##     time   error.diff shapes
##  1:  1.5 -0.041797918     16
##  2:  4.5 -0.031870515     16
##  3:  7.5 -0.018540309     16
##  4: 10.5  0.017660547     16
##  5: 13.5 -0.006657395     16
##  6: 16.5  0.002730878     16
##  7: 19.5 -0.035624644     16
##  8: 22.5 -0.014471503     16
##  9: 25.5  0.005341319     16
## 10: 28.5  0.002547420     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4630000     0.5983941 -0.14651688 100 1.5e-05 ***
##  2:      4.5      0.5066667     0.6266344 -0.10328499 150 1.9e-07 ***
##  3:      7.5      0.4593333     0.5432511 -0.08186935 150 0.00012 ***
##  4:     10.5      0.5133333     0.5865266 -0.06779861 150 0.00047 ***
##  5:     13.5      0.4680000     0.5743050 -0.09318691 150 8.6e-07 ***
##  6:     16.5      0.4200000     0.5144528 -0.09807768 150 1.2e-05 ***
##  7:     19.5      0.4826667     0.5502108 -0.05423641 150   0.0014 **
##  8:     22.5      0.4940000     0.5704597 -0.06446388 150   0.0018 **
##  9:     25.5      0.5406667     0.5923116 -0.03496485 150     0.044 *
## 10:     28.5      0.4966667     0.5699890 -0.06716888 150   0.0014 **
##     time  error.diff shapes
##  1:  1.5 -0.14651688     24
##  2:  4.5 -0.10328499     24
##  3:  7.5 -0.08186935     24
##  4: 10.5 -0.06779861     24
##  5: 13.5 -0.09318691     24
##  6: 16.5 -0.09807768     24
##  7: 19.5 -0.05423641     24
##  8: 22.5 -0.06446388     24
##  9: 25.5 -0.03496485     24
## 10: 28.5 -0.06716888     24

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.4355769     0.5969130 -0.167882862 104 3.2e-06 ***
##  2:      4.5      0.5089744     0.6297636 -0.133755664 156 3.6e-06 ***
##  3:      7.5      0.5102564     0.5544687 -0.055664136 156     0.036 *
##  4:     10.5      0.5224359     0.5229882 -0.002885828 156     0.89 :(
##  5:     13.5      0.5173077     0.5312208 -0.020469229 156     0.44 :(
##  6:     16.5      0.5102564     0.5008164  0.003037161 156     0.91 :(
##  7:     19.5      0.4576923     0.4456698  0.001732470 156     0.95 :(
##  8:     22.5      0.4211538     0.4198655 -0.005254933 156     0.84 :(
##  9:     25.5      0.4576923     0.3963862  0.067659240 156     0.015 *
## 10:     28.5      0.4435897     0.3637653  0.061888038 156     0.014 *
##     time   error.diff shapes
##  1:  1.5 -0.167882862     24
##  2:  4.5 -0.133755664     24
##  3:  7.5 -0.055664136     24
##  4: 10.5 -0.002885828     16
##  5: 13.5 -0.020469229     16
##  6: 16.5  0.003037161     16
##  7: 19.5  0.001732470     16
##  8: 22.5 -0.005254933     16
##  9: 25.5  0.067659240     24
## 10: 28.5  0.061888038     24

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79692  -0.20881  -0.03313   0.23079   0.61449  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20423    0.03167   6.449 1.87e-10 ***
## timeNorm     0.10792    0.03189   3.384 0.000746 ***
## obj.diff    -0.50984    0.03213 -15.870  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0678369)
## 
##     Null deviance: 77.814  on 869  degrees of freedom
## Residual deviance: 58.815  on 867  degrees of freedom
## AIC: 133.08
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.76293  -0.20557  -0.00186   0.21920   0.78230  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.13745    0.01980   6.943 5.42e-12 ***
## timeNorm     0.04599    0.02361   1.948   0.0515 .  
## obj.diff    -0.37363    0.02223 -16.804  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07290521)
## 
##     Null deviance: 146.38  on 1710  degrees of freedom
## Residual deviance: 124.52  on 1708  degrees of freedom
## AIC: 380.19
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.75342  -0.17252  -0.02447   0.19891   0.73840  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.10259    0.01707   6.009 2.27e-09 ***
## timeNorm     0.03827    0.02207   1.734   0.0831 .  
## obj.diff    -0.29614    0.02181 -13.579  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06371884)
## 
##     Null deviance: 123.60  on 1739  degrees of freedom
## Residual deviance: 110.68  on 1737  degrees of freedom
## AIC: 152.21
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5466667     0.7672967 -0.23051505 60 1.8e-07 ***
##  2:      4.5      0.5833333     0.7745990 -0.21375202 90   3e-07 ***
##  3:      7.5      0.6222222     0.7636568 -0.15560008 90   2e-05 ***
##  4:     10.5      0.6400000     0.7255871 -0.08870846 90     0.011 *
##  5:     13.5      0.6333333     0.7429823 -0.12560024 90 0.00023 ***
##  6:     16.5      0.6100000     0.7084840 -0.11082760 90   0.0015 **
##  7:     19.5      0.6266667     0.7018037 -0.07794421 90   0.0061 **
##  8:     22.5      0.6433333     0.7303487 -0.08962436 90     0.018 *
##  9:     25.5      0.6166667     0.6852720 -0.05557755 90     0.12 :(
## 10:     28.5      0.6222222     0.6567200 -0.02571537 90     0.42 :(
##     time  error.diff shapes
##  1:  1.5 -0.23051505     24
##  2:  4.5 -0.21375202     24
##  3:  7.5 -0.15560008     24
##  4: 10.5 -0.08870846     24
##  5: 13.5 -0.12560024     24
##  6: 16.5 -0.11082760     24
##  7: 19.5 -0.07794421     24
##  8: 22.5 -0.08962436     24
##  9: 25.5 -0.05557755     16
## 10: 28.5 -0.02571537     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.4906780     0.6127289 -0.121863522 118 0.00016 ***
##  2:      4.5      0.5587571     0.6718323 -0.106275573 177 9.3e-07 ***
##  3:      7.5      0.4920904     0.5246039 -0.039865590 177     0.066 .
##  4:     10.5      0.5491525     0.5743558 -0.021791442 177     0.33 :(
##  5:     13.5      0.5197740     0.5603081 -0.036992128 177     0.058 .
##  6:     16.5      0.4937853     0.5302803 -0.038144017 177     0.067 .
##  7:     19.5      0.4943503     0.5431448 -0.048797598 177     0.019 *
##  8:     22.5      0.4491525     0.4962824 -0.054945511 177     0.021 *
##  9:     25.5      0.5254237     0.5237839 -0.003542356 177     0.85 :(
## 10:     28.5      0.4971751     0.5007172 -0.013170703 177     0.54 :(
##     time   error.diff shapes
##  1:  1.5 -0.121863522     24
##  2:  4.5 -0.106275573     24
##  3:  7.5 -0.039865590     16
##  4: 10.5 -0.021791442     16
##  5: 13.5 -0.036992128     16
##  6: 16.5 -0.038144017     16
##  7: 19.5 -0.048797598     24
##  8: 22.5 -0.054945511     24
##  9: 25.5 -0.003542356     16
## 10: 28.5 -0.013170703     16

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n    pval
##  1:      1.5      0.4316667     0.4932968 -0.053470010 120 0.047 *
##  2:      4.5      0.4411111     0.4704908 -0.029889372 180 0.15 :(
##  3:      7.5      0.4283333     0.4517910 -0.020221640 180 0.31 :(
##  4:     10.5      0.4438889     0.4328301  0.013118846 180  0.5 :(
##  5:     13.5      0.4088889     0.4213415 -0.011758996 180 0.59 :(
##  6:     16.5      0.4150000     0.3990571  0.011179763 180  0.6 :(
##  7:     19.5      0.3888889     0.3755408  0.003628597 180 0.87 :(
##  8:     22.5      0.3844444     0.3692037  0.008763343 180 0.61 :(
##  9:     25.5      0.3950000     0.3494232  0.039735046 180 0.035 *
## 10:     28.5      0.3566667     0.3240913  0.014827565 180 0.44 :(
##     time   error.diff shapes
##  1:  1.5 -0.053470010     24
##  2:  4.5 -0.029889372     16
##  3:  7.5 -0.020221640     16
##  4: 10.5  0.013118846     16
##  5: 13.5 -0.011758996     16
##  6: 16.5  0.011179763     16
##  7: 19.5  0.003628597     16
##  8: 22.5  0.008763343     16
##  9: 25.5  0.039735046     24
## 10: 28.5  0.014827565     16

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.72641  -0.18104   0.07896   0.17901   0.32506  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.17441    0.11392   1.531   0.1280  
## timeNorm     0.05973    0.06311   0.946   0.3455  
## obj.diff    -0.34358    0.13212  -2.600   0.0103 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04409487)
## 
##     Null deviance: 6.6352  on 144  degrees of freedom
## Residual deviance: 6.2615  on 142  degrees of freedom
## AIC: -36.144
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean    error.diff  n    pval
##  1:      1.5      0.7000000     0.8422160 -0.1313282565 10 0.084 .
##  2:      4.5      0.7200000     0.8045511 -0.0795853830 15 0.49 :(
##  3:      7.5      0.6933333     0.7637929 -0.0692528749 15 0.25 :(
##  4:     10.5      0.7200000     0.7894410 -0.0625540247 15 0.36 :(
##  5:     13.5      0.7000000     0.8006171 -0.1084499094 15 0.055 .
##  6:     16.5      0.7200000     0.7661172 -0.0140493178 15  0.8 :(
##  7:     19.5      0.7466667     0.7396280  0.0120888700 15  0.8 :(
##  8:     22.5      0.7333333     0.7489324 -0.0006995672 15    1 :(
##  9:     25.5      0.7533333     0.8163298 -0.0314486693 15  0.6 :(
## 10:     28.5      0.6866667     0.7440259 -0.0101905183 15 0.85 :(
##     time    error.diff shapes
##  1:  1.5 -0.1313282565     16
##  2:  4.5 -0.0795853830     16
##  3:  7.5 -0.0692528749     16
##  4: 10.5 -0.0625540247     16
##  5: 13.5 -0.1084499094     16
##  6: 16.5 -0.0140493178     16
##  7: 19.5  0.0120888700     16
##  8: 22.5 -0.0006995672     16
##  9: 25.5 -0.0314486693     16
## 10: 28.5 -0.0101905183     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7008  -0.1756   0.0104   0.2007   0.6764  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.129171   0.042821   3.017  0.00266 ** 
## timeNorm    -0.001606   0.039220  -0.041  0.96736    
## obj.diff    -0.312573   0.058219  -5.369 1.13e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06994394)
## 
##     Null deviance: 44.480  on 608  degrees of freedom
## Residual deviance: 42.386  on 606  degrees of freedom
## AIC: 113.28
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.5214286     0.6226413 -0.09427256 42   0.054 .
##  2:      4.5      0.5476190     0.6192837 -0.06506441 63   0.076 .
##  3:      7.5      0.5222222     0.5488374 -0.02194671 63   0.54 :(
##  4:     10.5      0.5269841     0.5633358 -0.01792603 63   0.63 :(
##  5:     13.5      0.5365079     0.5457213 -0.00376177 63   0.93 :(
##  6:     16.5      0.5285714     0.5497442 -0.02446941 63    0.5 :(
##  7:     19.5      0.4698413     0.5571074 -0.09226440 63 0.0066 **
##  8:     22.5      0.4412698     0.5026155 -0.06648564 63   0.067 .
##  9:     25.5      0.4777778     0.4906858 -0.01544805 63   0.67 :(
## 10:     28.5      0.4777778     0.4965908 -0.02412439 63   0.42 :(
##     time  error.diff shapes
##  1:  1.5 -0.09427256     16
##  2:  4.5 -0.06506441     16
##  3:  7.5 -0.02194671     16
##  4: 10.5 -0.01792603     16
##  5: 13.5 -0.00376177     16
##  6: 16.5 -0.02446941     16
##  7: 19.5 -0.09226440     24
##  8: 22.5 -0.06648564     16
##  9: 25.5 -0.01544805     16
## 10: 28.5 -0.02412439     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.66104  -0.16469  -0.00053   0.17110   0.56752  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003495   0.031165   0.112    0.911
## timeNorm    0.030048   0.032813   0.916    0.360
## obj.diff    0.014011   0.048027   0.292    0.771
## 
## (Dispersion parameter for gaussian family taken to be 0.04854566)
## 
##     Null deviance: 29.460  on 608  degrees of freedom
## Residual deviance: 29.419  on 606  degrees of freedom
## AIC: -109.12
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.5238095     0.5010468  0.029289622 42 0.44 :(
##  2:      4.5      0.4777778     0.4761135  0.006623743 63 0.82 :(
##  3:      7.5      0.4507937     0.4585390 -0.004803129 63  0.9 :(
##  4:     10.5      0.5111111     0.4440686  0.079755085 63 0.014 *
##  5:     13.5      0.4349206     0.4202938  0.019074304 63 0.57 :(
##  6:     16.5      0.4809524     0.4449609  0.036494116 63 0.21 :(
##  7:     19.5      0.4650794     0.4547299  0.003733181 63 0.89 :(
##  8:     22.5      0.4444444     0.4137030  0.029021770 63 0.27 :(
##  9:     25.5      0.4079365     0.3720518  0.034671878 63 0.19 :(
## 10:     28.5      0.3825397     0.3393145  0.035270385 63 0.18 :(
##     time   error.diff shapes
##  1:  1.5  0.029289622     16
##  2:  4.5  0.006623743     16
##  3:  7.5 -0.004803129     16
##  4: 10.5  0.079755085     24
##  5: 13.5  0.019074304     16
##  6: 16.5  0.036494116     16
##  7: 19.5  0.003733181     16
##  8: 22.5  0.029021770     16
##  9: 25.5  0.034671878     16
## 10: 28.5  0.035270385     16

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79170  -0.22815  -0.02594   0.22945   0.66500  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.12235    0.03937   3.108  0.00203 ** 
## timeNorm     0.07607    0.04697   1.620  0.10616    
## obj.diff    -0.40201    0.03940 -10.203  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06464184)
## 
##     Null deviance: 31.141  on 376  degrees of freedom
## Residual deviance: 24.176  on 374  degrees of freedom
## AIC: 42.305
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.4961538     0.6466973 -0.16598859 26   0.011 *
##  2:      4.5      0.5461538     0.6931430 -0.15083147 39 0.0035 **
##  3:      7.5      0.5692308     0.7162191 -0.16082632 39  0.002 **
##  4:     10.5      0.6000000     0.7033832 -0.10206206 39   0.036 *
##  5:     13.5      0.6128205     0.7150475 -0.09234508 39   0.026 *
##  6:     16.5      0.4897436     0.6162529 -0.15463914 39 0.0075 **
##  7:     19.5      0.5410256     0.6459039 -0.11889761 39   0.028 *
##  8:     22.5      0.6923077     0.7502411 -0.04716618 39    0.4 :(
##  9:     25.5      0.5512821     0.6511959 -0.08808730 39   0.082 .
## 10:     28.5      0.5589744     0.6283476 -0.05670973 39   0.24 :(
##     time  error.diff shapes
##  1:  1.5 -0.16598859     24
##  2:  4.5 -0.15083147     24
##  3:  7.5 -0.16082632     24
##  4: 10.5 -0.10206206     24
##  5: 13.5 -0.09234508     24
##  6: 16.5 -0.15463914     24
##  7: 19.5 -0.11889761     24
##  8: 22.5 -0.04716618     16
##  9: 25.5 -0.08808730     16
## 10: 28.5 -0.05670973     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.8010  -0.1602  -0.0070   0.1883   0.8299  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.01962    0.02788   0.704   0.4818    
## timeNorm     0.06313    0.03687   1.712   0.0873 .  
## obj.diff    -0.22358    0.02870  -7.790 2.46e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07348453)
## 
##     Null deviance: 55.675  on 695  degrees of freedom
## Residual deviance: 50.925  on 693  degrees of freedom
## AIC: 163.12
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.4833333     0.6172699 -0.143174886 48   0.0045 **
##  2:      4.5      0.5569444     0.6703581 -0.082514708 72 0.00014 ***
##  3:      7.5      0.4277778     0.4814532 -0.059820535 72     0.038 *
##  4:     10.5      0.5291667     0.6061622 -0.063605592 72      0.02 *
##  5:     13.5      0.4666667     0.5570867 -0.074844866 72   0.0036 **
##  6:     16.5      0.4152778     0.4912136 -0.068363271 72     0.019 *
##  7:     19.5      0.5222222     0.5512350 -0.009025565 72     0.66 :(
##  8:     22.5      0.4069444     0.5030260 -0.097633990 72   0.0027 **
##  9:     25.5      0.5680556     0.6007228 -0.021474609 72     0.34 :(
## 10:     28.5      0.5361111     0.5833722 -0.049015654 72     0.061 .
##     time   error.diff shapes
##  1:  1.5 -0.143174886     24
##  2:  4.5 -0.082514708     24
##  3:  7.5 -0.059820535     24
##  4: 10.5 -0.063605592     24
##  5: 13.5 -0.074844866     24
##  6: 16.5 -0.068363271     24
##  7: 19.5 -0.009025565     16
##  8: 22.5 -0.097633990     24
##  9: 25.5 -0.021474609     16
## 10: 28.5 -0.049015654     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.67969  -0.14493  -0.06193   0.24483   0.77309  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.03665    0.03281   1.117    0.265    
## timeNorm     0.02377    0.04690   0.507    0.613    
## obj.diff    -0.28843    0.03579  -8.059 1.05e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06447889)
## 
##     Null deviance: 28.311  on 376  degrees of freedom
## Residual deviance: 24.115  on 374  degrees of freedom
## AIC: 41.353
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.3923077     0.5152434 -0.11294714 26   0.046 *
##  2:      4.5      0.3743590     0.4794053 -0.08723250 39   0.027 *
##  3:      7.5      0.4076923     0.4843714 -0.06343240 39   0.093 .
##  4:     10.5      0.3974359     0.4334195 -0.04546501 39   0.15 :(
##  5:     13.5      0.3256410     0.4653501 -0.14055051 39 0.0019 **
##  6:     16.5      0.3589744     0.4555558 -0.09769236 39 0.0072 **
##  7:     19.5      0.3512821     0.4526269 -0.08381314 39 0.0024 **
##  8:     22.5      0.4564103     0.5151715 -0.02865573 39   0.42 :(
##  9:     25.5      0.4794872     0.5178990 -0.02246319 39   0.48 :(
## 10:     28.5      0.3615385     0.4869228 -0.11144309 39 0.0018 **
##     time  error.diff shapes
##  1:  1.5 -0.11294714     24
##  2:  4.5 -0.08723250     24
##  3:  7.5 -0.06343240     16
##  4: 10.5 -0.04546501     16
##  5: 13.5 -0.14055051     24
##  6: 16.5 -0.09769236     24
##  7: 19.5 -0.08381314     24
##  8: 22.5 -0.02865573     16
##  9: 25.5 -0.02246319     16
## 10: 28.5 -0.11144309     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.71273  -0.14992  -0.08786   0.27418   0.49321  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.42032    0.06182   6.799 4.66e-11 ***
## timeNorm     0.10387    0.05362   1.937   0.0535 .  
## obj.diff    -0.79834    0.06101 -13.085  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07238212)
## 
##     Null deviance: 39.631  on 347  degrees of freedom
## Residual deviance: 24.972  on 345  degrees of freedom
## AIC: 78.791
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.5375000     0.8667296 -0.336223194 24 8.3e-07 ***
##  2:      4.5      0.5666667     0.8503630 -0.300166011 36 8.1e-06 ***
##  3:      7.5      0.6500000     0.8149910 -0.191351305 36   0.0067 **
##  4:     10.5      0.6500000     0.7230354 -0.075959113 36     0.21 :(
##  5:     13.5      0.6277778     0.7492306 -0.168314679 36     0.043 *
##  6:     16.5      0.6944444     0.7843873 -0.103751078 36      0.1 :(
##  7:     19.5      0.6694444     0.7466016 -0.071855795 36     0.088 .
##  8:     22.5      0.5527778     0.7010553 -0.155156364 36     0.017 *
##  9:     25.5      0.6305556     0.6675804 -0.011808735 36     0.88 :(
## 10:     28.5      0.6638889     0.6510792  0.008180296 36     0.87 :(
##     time   error.diff shapes
##  1:  1.5 -0.336223194     24
##  2:  4.5 -0.300166011     24
##  3:  7.5 -0.191351305     24
##  4: 10.5 -0.075959113     16
##  5: 13.5 -0.168314679     24
##  6: 16.5 -0.103751078     16
##  7: 19.5 -0.071855795     16
##  8: 22.5 -0.155156364     24
##  9: 25.5 -0.011808735     16
## 10: 28.5  0.008180296     16

## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.64791  -0.12252  -0.01687   0.08254   0.56550  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.412353   0.037259  11.067   <2e-16 ***
## timeNorm    -0.004306   0.043597  -0.099    0.921    
## obj.diff    -0.758214   0.039233 -19.326   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.0555671)
## 
##     Null deviance: 44.748  on 405  degrees of freedom
## Residual deviance: 22.394  on 403  degrees of freedom
## AIC: -16.24
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.4571429     0.5900756 -0.128331981 28     0.074 .
##  2:      4.5      0.5785714     0.7531825 -0.175486570 42 0.00038 ***
##  3:      7.5      0.5571429     0.5622264 -0.024952320 42     0.62 :(
##  4:     10.5      0.6166667     0.5363606  0.074535928 42     0.26 :(
##  5:     13.5      0.5857143     0.5877105 -0.004272844 42     0.89 :(
##  6:     16.5      0.5761905     0.5680560 -0.005940807 42     0.91 :(
##  7:     19.5      0.4833333     0.5083317 -0.026640701 42     0.56 :(
##  8:     22.5      0.5333333     0.4752222  0.063331693 42      0.3 :(
##  9:     25.5      0.5238095     0.4415357  0.079086797 42     0.13 :(
## 10:     28.5      0.4595238     0.3652122  0.102052174 42     0.089 .
##     time   error.diff shapes
##  1:  1.5 -0.128331981     16
##  2:  4.5 -0.175486570     24
##  3:  7.5 -0.024952320     16
##  4: 10.5  0.074535928     16
##  5: 13.5 -0.004272844     16
##  6: 16.5 -0.005940807     16
##  7: 19.5 -0.026640701     16
##  8: 22.5  0.063331693     16
##  9: 25.5  0.079086797     16
## 10: 28.5  0.102052174     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.65768  -0.19763  -0.04035   0.21009   0.72371  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.12696    0.02761   4.598    5e-06 ***
## timeNorm     0.06463    0.03613   1.789    0.074 .  
## obj.diff    -0.37517    0.03533 -10.619   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06922719)
## 
##     Null deviance: 61.766  on 753  degrees of freedom
## Residual deviance: 51.990  on 751  degrees of freedom
## AIC: 131.3
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.3769231     0.4760639 -0.102437219 52 0.042 *
##  2:      4.5      0.4448718     0.4614922 -0.027759149 78 0.44 :(
##  3:      7.5      0.4205128     0.4300504 -0.017443018 78 0.62 :(
##  4:     10.5      0.4128205     0.4234581 -0.008719531 78 0.81 :(
##  5:     13.5      0.4294872     0.4001833  0.034200208 78 0.44 :(
##  6:     16.5      0.3897436     0.3337317  0.052227726 78 0.13 :(
##  7:     19.5      0.3461538     0.2730373  0.067476620 78 0.073 .
##  8:     22.5      0.3000000     0.2602781  0.014479723 78 0.59 :(
##  9:     25.5      0.3423077     0.2469083  0.092629303 78 0.011 *
## 10:     28.5      0.3333333     0.2303798  0.075981671 78 0.048 *
##     time   error.diff shapes
##  1:  1.5 -0.102437219     24
##  2:  4.5 -0.027759149     16
##  3:  7.5 -0.017443018     16
##  4: 10.5 -0.008719531     16
##  5: 13.5  0.034200208     16
##  6: 16.5  0.052227726     16
##  7: 19.5  0.067476620     16
##  8: 22.5  0.014479723     16
##  9: 25.5  0.092629303     24
## 10: 28.5  0.075981671     24

Link between d(t-1) and error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.2689, p-value = 0.02327
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04143664 
## 
## [1] "DTM 0.041 0.023 *"

## Résultat test Kendall erreur basée sur mise avec confiance estimée : 0.041 0.023 *

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -27.959, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.5375531 
## 
## [1] "DTM -0.54 5.2e-172 ***"

## Résultat test Kendall erreur basée sur échelle de confiance avec confiance estimée : -0.54 5.2e-172 ***

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.83838, p-value = 0.4018
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01495302

## Résultat test Kendall erreur basée sur mise avec confiance estimée : 0.015 0.4 :(

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -17.399, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.3311254 
## 
## [1] "DTS -0.33 8.4e-68 ***"

## Résultat test Kendall erreur basée sur échelle de confiance avec confiance estimée : -0.33 8.4e-68 ***

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 3.8927, p-value = 9.912e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.06851885 
## 
## [1] "DTL 0.069 9.9e-05 ***"

## Résultat test Kendall erreur basée sur mise avec confiance estimée : 0.069 9.9e-05 ***

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -17.234, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.3231389 
## 
## [1] "DTL -0.32 1.5e-66 ***"

## Résultat test Kendall erreur basée sur échelle de confiance avec confiance estimée : -0.32 1.5e-66 ***

Subjective difficulty and play habits

Playing video game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.9512, p-value = 0.05103
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1244062 
## 
## [1] "pvg.on.error -0.12 0.051 ."

Playing board game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.611, p-value = 0.009028
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1640952 
## 
## [1] "pbg.on.error -0.16 0.009 **"

In game level and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.4956, p-value = 0.1348
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.08314103

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## T = 516, p-value = 0.6615
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.0453284

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.1962, p-value = 0.2316
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1167347

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.7676, p-value = 0.07713
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1689291 
## 
## [1] "niveau.group.on.error.l 0.17 0.077 ."

Sex and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 3.4092, p-value = 0.0006514
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2309816 
## 
## [1] "sexe.on.error 0.23 0.00065 ***"

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.8377, p-value = 0.06611
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2235683 
## 
## [1] "sexe.on.error.m 0.22 0.066 ."

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.1421, p-value = 0.03218
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2523479 
## 
## [1] "sexe.on.error.s 0.25 0.032 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.9042, p-value = 0.05688
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##      tau 
## 0.219795 
## 
## [1] "sexe.on.error.l 0.22 0.057 ."

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 3351, p-value = 0.0006562
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.01884107 0.09421998
## sample estimates:
## difference in location 
##              0.0580818 
## 
## [1] "sexe.on.error.2 0.058 0.00066 *** mean(A): -0.057 mean(B): -0.00063"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 338, p-value = 0.06742
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.002139502  0.112594328
## sample estimates:
## difference in location 
##             0.05082362 
## 
## [1] "sexe.on.error.m.2 0.051 0.067 . mean(A): -0.052 mean(B): 0.0015"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 375, p-value = 0.032
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.004691109 0.132198686
## sample estimates:
## difference in location 
##             0.06809898 
## 
## [1] "sexe.on.error.s.2 0.068 0.032 * mean(A): -0.058 mean(B): 0.008"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 405, p-value = 0.05764
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.001446698  0.111180497
## sample estimates:
## difference in location 
##             0.05042229 
## 
## [1] "sexe.on.error.l.2 0.05 0.058 . mean(A): -0.061 mean(B): -0.01"

Risk aversion and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.69771, p-value = 0.4854
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04215397

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.43467, p-value = 0.6638
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.04748814

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.73768, p-value = 0.4607
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07731606

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.85186, p-value = 0.3943
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.08786374

Self efficacy and subjective difficulty error

## Warning: Removed 72 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -3.3268, p-value = 0.0008786
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2704897 
## 
## [1] "self.eff.on.error -0.27 0.00088 ***"

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 23 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.8964, p-value = 0.0579
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2826236 
## 
## [1] "self.eff.on.error -0.28 0.058 ."

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 23 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.7201, p-value = 0.08542
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2402101 
## 
## [1] "self.eff.on.error -0.24 0.085 ."

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 26 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.0192, p-value = 0.04346
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2880916 
## 
## [1] "self.eff.on.error -0.29 0.043 *"